

'''simple docstring'''






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
 raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     =						logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =						"""Hello world! cécé herlolip"""



def 			__a		(       lowerCAmelCase__ :    Dict     ,  lowerCAmelCase__ :    Optional[Any]     ,  lowerCAmelCase__ :    Dict      ):
     a__   :      Dict     =						FairseqRobertaModel.from_pretrained(lowerCAmelCase__      )
     roberta.eval()  # disable dropout
     a__   :      str     =						roberta.model.encoder.sentence_encoder
     a__   :      Tuple     =						XLMRobertaConfig(
         vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,  hidden_size=roberta.cfg.model.encoder_embed_dim     ,  num_hidden_layers=roberta.cfg.model.encoder_layers     ,  num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,  intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,  max_position_embeddings=514     ,  type_vocab_size=1     ,  layer_norm_eps=1E-5     ,  )
     if classification_head:
          a__   :      int     =						roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

     print('''Our RoBERTa config:'''     ,  lowerCAmelCase__      )

     a__   :      Optional[int]     =						XLMRobertaXLForSequenceClassification(lowerCAmelCase__      ) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase__      )
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     a__   :      Dict     =						roberta_sent_encoder.embed_tokens.weight
     a__   :      int     =						roberta_sent_encoder.embed_positions.weight
     a__   :      Union[str, Any]     =						torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c RoBERTa doesn't use them.

     a__   :      int     =						roberta_sent_encoder.layer_norm.weight
     a__   :      List[Any]     =						roberta_sent_encoder.layer_norm.bias

     for i in range(config.num_hidden_layers      ):
          # Encoder: start of layer
          a__   :      Optional[int]     =						model.roberta.encoder.layer[i]
          a__   :      Tuple     =						roberta_sent_encoder.layers[i]

          a__   :      List[Any]     =						layer.attention
          a__   :      Union[str, Any]     =						roberta_layer.self_attn_layer_norm.weight
          a__   :      List[Any]     =						roberta_layer.self_attn_layer_norm.bias

          # self attention
          a__   :      int     =						layer.attention.self
          assert (
              roberta_layer.self_attn.k_proj.weight.data.shape
              == roberta_layer.self_attn.q_proj.weight.data.shape
              == roberta_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)      )
          )

          a__   :      Optional[int]     =						roberta_layer.self_attn.q_proj.weight
          a__   :      Optional[int]     =						roberta_layer.self_attn.q_proj.bias
          a__   :      Dict     =						roberta_layer.self_attn.k_proj.weight
          a__   :      Tuple     =						roberta_layer.self_attn.k_proj.bias
          a__   :      Optional[Any]     =						roberta_layer.self_attn.v_proj.weight
          a__   :      Tuple     =						roberta_layer.self_attn.v_proj.bias

          # self-attention output
          a__   :      Dict     =						layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          a__   :      Union[str, Any]     =						roberta_layer.self_attn.out_proj.weight
          a__   :      Dict     =						roberta_layer.self_attn.out_proj.bias

          # this one is final layer norm
          a__   :      Tuple     =						roberta_layer.final_layer_norm.weight
          a__   :      Optional[int]     =						roberta_layer.final_layer_norm.bias

          # intermediate
          a__   :      Any     =						layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          a__   :      List[str]     =						roberta_layer.fca.weight
          a__   :      Dict     =						roberta_layer.fca.bias

          # output
          a__   :      Dict     =						layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          a__   :      Any     =						roberta_layer.fca.weight
          a__   :      Optional[Any]     =						roberta_layer.fca.bias
          # end of layer

     if classification_head:
          a__   :      List[str]     =						roberta.model.classification_heads['''mnli'''].dense.weight
          a__   :      List[str]     =						roberta.model.classification_heads['''mnli'''].dense.bias
          a__   :      Tuple     =						roberta.model.classification_heads['''mnli'''].out_proj.weight
          a__   :      Union[str, Any]     =						roberta.model.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          a__   :      Union[str, Any]     =						roberta.model.encoder.lm_head.dense.weight
          a__   :      Optional[Any]     =						roberta.model.encoder.lm_head.dense.bias
          a__   :      List[str]     =						roberta.model.encoder.lm_head.layer_norm.weight
          a__   :      str     =						roberta.model.encoder.lm_head.layer_norm.bias
          a__   :      Dict     =						roberta.model.encoder.lm_head.weight
          a__   :      Optional[int]     =						roberta.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     a__   :      Any     =						roberta.encode(lowerCAmelCase__      ).unsqueeze(0      )  # batch of size 1

     a__   :      Union[str, Any]     =						model(lowerCAmelCase__      )[0]
     if classification_head:
          a__   :      Optional[int]     =						roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowerCAmelCase__      )      )
     else:
          a__   :      Dict     =						roberta.model(lowerCAmelCase__      )[0]
     print(our_output.shape     ,  their_output.shape      )
     a__   :      Any     =						torch.max(torch.abs(our_output - their_output      )      ).item()
     print(F'max_absolute_diff = {max_absolute_diff}'      )  # ~ 1e-7
     a__   :      Dict     =						torch.allclose(lowerCAmelCase__     ,  lowerCAmelCase__     ,  atol=1E-3      )
     print('''Do both models output the same tensors?'''     ,  '''🔥''' if success else '''💩'''      )
     if not success:
          raise Exception('''Something went wRoNg'''      )

     pathlib.Path(lowerCAmelCase__      ).mkdir(parents=lowerCAmelCase__     ,  exist_ok=lowerCAmelCase__      )
     print(F'Saving model to {pytorch_dump_folder_path}'      )
     model.save_pretrained(lowerCAmelCase__      )


if __name__ == "__main__":
 __SCREAMING_SNAKE_CASE     =						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--classification_head', action='store_true', help='Whether to convert a final classification head.'
 )
 __SCREAMING_SNAKE_CASE     =						parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )

def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				if not isinstance(lowercase				,     lowercase				):
								raise ValueError('''Input must be an integer'''				)
				if input_num <= 0:
								raise ValueError('''Input must be positive'''				)
				return sum(
				    divisor for divisor in range(1				,     input_num // 2 + 1				) if input_num % divisor == 0				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()